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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to cloud computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in mobile edge computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among cloud or mobile edge computing (MEC) server or local IoT devices to achieve better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%.

Details

Title
An Algorithm to Minimize Energy Consumption and Elapsed Time for IoT Workloads in a Hybrid Architecture
Author
Julio C S dos Anjos 1   VIAFID ORCID Logo  ; Gross, João L G 1   VIAFID ORCID Logo  ; Matteussi, Kassiano J 1   VIAFID ORCID Logo  ; González, Gabriel V 2   VIAFID ORCID Logo  ; Valderi R Q Leithardt 3   VIAFID ORCID Logo  ; Geyer, Claudio F R 1   VIAFID ORCID Logo 

 Institute of Informatics, UFRGS/PPGC, Federal University of Rio Grande do Sul, RS, Porto Alegre 91501-970, Brazil; [email protected] (J.L.G.G.); [email protected] (K.J.M.); [email protected] (C.F.R.G.) 
 Faculty of Science, Expert Systems and Applications Laboratory, University of Salamanca, 37008 Salamanca, Spain; [email protected] 
 COPELABS, Universidade Lusófona de Humanidades e Tecnologias, 1749-024 Lisboa, Portugal; [email protected]; VALORIZA, Research Center for Endogenous Resource Valorization, Polytechnic Institute of Portalegre, 7300-555 Portalegre, Portugal 
First page
2914
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2530164536
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.